POLYCYCLIC AROMATIC HYDROCARBONS: ULTRASENSITIVE DETECTION, EARLY LIFE EXPOSURES-CLINICAL OUTCOMES (PRETERM BIRTHS, CHRONIC LUNG DISEASE, AND NEUROCOGNITIVE DEFICITS), PREVENTION AND REMEDIATION

多环芳烃：超灵敏检测、生命早期暴露-临床结果（早产、慢性肺病和神经认知缺陷）、预防和补救

• 批准号: 10401127
• 负责人: BHAGAVATULA MOORTHY
• 金额: --
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-28 至 2021-07-24
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10401127
• 关键词: Air; Area; Aromatic Compounds; Aromatic Polycyclic Hydrocarbons; Benchmarking; Biological; Chemicals; Chemistry; Chronic lung disease; Clinical; Clutterings; Complex; Data; Data Science; Detection; Development; Effectiveness; Environment; Family; Foundations; Geometry; Goals; Health; Human; Image; Investigation; Machine Learning; Mass Fragmentography; Methods; Molecular; Monitor; Neurocognitive Deficit; Outcome; Parents; Partition Coefficient; Patient Monitoring; Polymers; Premature Birth; Prevention; Research; Sampling; Soil; Spectrum Analysis; Surface; Testing; Training; Variant; Visual; Water; Work; base; design; detection sensitivity; early life exposure; effectiveness testing; exposed human population; hazard; improved; innovation; interest; learning classifier; machine learning algorithm; machine learning method; monolayer; nanoparticle; remediation; spectroscopic survey; superfund site; surface coating; synergism; tool; training opportunity; vibration

Project Summary The overarching goal of this project is the development of new and innovative approaches to ultrasensitive detection and identification of polycyclic aromatic hydrocarbon (PAH) molecules and their functionalized derivatives (polycyclic aromatic compounds, or PACs). Ms. Mary Bajomo, will pursue research central to these project goals, as outlined by both Specific Aims. The Specific Aims of this Diversity Supplement form a central, essential subset of the work required to achieve the project goals, and provide an essential foundation for the sensing methods to be developed over the course of this project. They also lay the groundwork for bringing Machine Learning methods into the subfield of spectroscopic chemical sensing. Pursuing research at the interface between Chemistry and Machine Learning presents an exceptional training opportunity for Ms. Bajomo, and will allow her to interact strongly with the three PIs and their research groups in three fields: the Halas group, for experimental chemistry in the area of surface-enhanced spectroscopic sensing, the Nordlander group, on nanoparticle-based substrate design, and the Patel group, experts in Machine Learning and Data Science. Our hypothesis is that Machine Learning classifiers can be developed and used to distinguish between specific PAH and PAC molecules found in environmental or biological samples through their vibrational spectroscopic signatures. An essential aspect of this approach is the identification of PAH and PAC molecules while embedded in a molecular or polymer capture layer that has been designed to extract PAH/PAC molecules from environmental and/or biological samples that has its own specific spectroscopic signature “background”. These investigations are foundational to the detection of multicomponent mixtures of PAH/PAC molecules from realistic environmental or biological samples, using a combination of surface- enhanced spectroscopies and Machine Learning algorithms analogous to image recognition in cluttered, complex background environments. The two Specific Aims that Ms Bajomo will pursue are: Specific Aim 1: The identification and quantitative characterization of a universal capture layer for the wide range of PAH and PAC compounds encountered in biological and environmental samples. This capture layer would be serve as a universal coating for surface-enhanced Raman and Infrared spectroscopic substrates, and allow for the extraction of PAH and PAC molecules from solution in concentrations suitable for detection, consistent with concentrations of these chemicals found in samples of interest. Specific Aim 2: To develop surface-enhanced chemical sensing data as input to Machine Learning classifiers, to benchmark their effectiveness in identifying specific PAH molecules and distinguishing PAH/PAC molecules from each other by ML methods. This close synergy between experimental spectroscopic studies and ML classifier testing presents an outstanding opportunity for graduate training at the interface between two extremely important and dynamic research field for Ms. Bajomo.

项目概要 该项目的总体目标是开发新的创新方法来实现超灵敏 多环芳烃（PAH）分子及其功能化的检测与鉴定 Mary Bajomo 女士将从事这些领域的核心研究。 本多样性补充文件的具体目标概述了项目目标， 实现项目目标所需工作的重要子集，并为项目的实施奠定重要基础 他们还为该项目过程中开发的传感方法奠定了基础。 机器学习方法进入光谱化学传感子领域进行研究。 化学和机器学习之间的接口为女士提供了绝佳的培训机会。 Bajomo，将使她能够与三位 PI 及其在三个领域的研究小组进行密切互动： Halas 小组，针对表面增强光谱传感领域的实验化学， Nordlander 小组负责基于纳米粒子的基板设计，Patel 小组负责机器学习专家 我们的假设是机器学习分类器可以被开发并用于 通过以下方式区分环境或生物样品中发现的特定 PAH 和 PAC 分子 这种方法的一个重要方面是识别 PAH 和 PAC 分子嵌入分子或聚合物捕获层中，旨在提取 来自环境和/或生物样品的 PAH/PAC 分子，具有自己特定的光谱 这些研究是检测多组分混合物的基础。 PAH/PAC 分子来自真实的环境或生物样品，使用表面组合 增强的光谱学和机器学习算法，类似于杂乱的图像识别， Bajomo 女士将追求的两个具体目标是： 具体目标 1： 对多种 PAH 和 PAC 的通用捕获层进行识别和定量表征 该捕获层将用作生物和环境样品中遇到的化合物。 适用于表面增强拉曼和红外光谱基底的通用涂层，并允许 从适合检测的浓度的溶液中提取 PAH 和 PAC 分子，与 在感兴趣的样品中发现这些化学物质的浓度。具体目标 2：开发表面增强材料。 化学传感数据作为机器学习分类器的输入，以衡量其识别有效性 特定的 PAH 分子以及通过 ML 方法区分 PAH/PAC 分子。 实验光谱研究和 ML 分类器测试之间的协同作用呈现出出色的效果 在两个极其重要且充满活力的研究领域之间进行研究生培训的机会 为巴乔莫女士。